Characterizations of Soil Collapsibility: Effect of Salts Dilution

Collapsibility of soils is the large change in volume of soil upon saturation or wetting.  This change may or may not be the result of the application of additional load.  Soil at a construction site may not always be suitable for supporting structures such as buildings, bridges, highways, and dams.  For example, if soil is placed in a certain none desire density, a large settlement will occur either due to loading or wetting of soil deposits.  Hence, a collapse will occur which will create a large subsidence or a sinkhole.In this study, soil samples of CL-ML soil were modified by adding different amounts of brine.  The main goal of which was to examine the effect of brine presence on the collapse potential of the soil.  Soil index properties, compaction characteristics, and a collapse potential were evaluated according to ASTM standards.  The test includes Atterberg's limit, Harvard miniature compaction, and double oedometer tests.It has been shown that brine additive has pronounced effect on the Atterberg’s limits; it is clearly shown that as the amount of brine increases both liquid limit and plastic limit decrease.  Compaction curve characteristics of soil were altered by the presence of brine, the maximum dry density, obtained using Harvard miniature device, increased as brine percentage increased, however, the optimum moisture content showed substantial decrease with increasing the amount of brine.

Characterizations of Soil Collapsibility: Effect of Salts Dilution

Collapsibility of soils is the large change in volume of soil upon saturation or wetting.  This change may or may not be the result of the application of additional load.  Soil at a construction site may not always be suitable for supporting structures such as buildings, bridges, highways, and dams.  For example, if soil is placed in a certain none desire density, a large settlement will occur either due to loading or wetting of soil deposits.  Hence, a collapse will occur which will create a large subsidence or a sinkhole.In this study, soil samples of CL-ML soil were modified by adding different amounts of brine.  The main goal of which was to examine the effect of brine presence on the collapse potential of the soil.  Soil index properties, compaction characteristics, and a collapse potential were evaluated according to ASTM standards.  The test includes Atterberg's limit, Harvard miniature compaction, and double oedometer tests.It has been shown that brine additive has pronounced effect on the Atterberg’s limits; it is clearly shown that as the amount of brine increases both liquid limit and plastic limit decrease.  Compaction curve characteristics of soil were altered by the presence of brine, the maximum dry density, obtained using Harvard miniature device, increased as brine percentage increased, however, the optimum moisture content showed substantial decrease with increasing the amount of brine.

Experimental Study on the Utilization of Fine Steel Slag on Stabilizing High Plastic Subgrade Soil

The three major steel manufacturing factories in Jordan dump their byproduct, steel slag, randomly in open areas, which causes many environmental hazardous problems. This study intended to explore the effectiveness of using fine steel slag aggregate (FSSA) in improving the geotechnical properties of high plastic subgrade soil. First soil and fine steel slag mechanical and engineering properties were evaluating. Then 0%, 5%, 10%, 15%, 20%, and 25% dry weight of soil of fine steel slag (FSSA) were added and mixed into the prepared soil samples. The effectiveness of the FSSA was judged by the improvement in consistency limits, compaction, free swell, unconfined compression strength, and California bearing ratio (CBR). From the test results, it is observed that 20% FSSA additives will reduce plasticity index and free swell by 26.3% and 58.3%, respectively. Furthermore, 20% FSSA additives will increase the unconfined compressive strength, maximum dry density, and CBR value by 100%, 6.9%, and 154%. By conclusion FSSA had a positive effect on the geotechnical properties of the soil and it can be used as admixture in proving geotechnical characteristics of subgrade soil, not only solving the waste disposal problem

Characterizations of Soil Collapsibility: Effect of Salts Dilution

Collapsibility of soils is the large change in volume of soil upon saturation or wetting.  This change may or may not be the result of the application of additional load.  Soil at a construction site may not always be suitable for supporting structures such as buildings, bridges, highways, and dams.  For example, if soil is placed in a certain none desire density, a large settlement will occur either due to loading or wetting of soil deposits.  Hence, a collapse will occur which will create a large subsidence or a sinkhole.In this study, soil samples of CL-ML soil were modified by adding different amounts of brine.  The main goal of which was to examine the effect of brine presence on the collapse potential of the soil.  Soil index properties, compaction characteristics, and a collapse potential were evaluated according to ASTM standards.  The test includes Atterberg's limit, Harvard miniature compaction, and double oedometer tests.It has been shown that brine additive has pronounced effect on the Atterberg’s limits; it is clearly shown that as the amount of brine increases both liquid limit and plastic limit decrease.  Compaction curve characteristics of soil were altered by the presence of brine, the maximum dry density, obtained using Harvard miniature device, increased as brine percentage increased, however, the optimum moisture content showed substantial decrease with increasing the amount of brine.

Experimental Study on the Utilization of Fine Steel Slag on Stabilizing High Plastic Subgrade Soil

The three major steel manufacturing factories in Jordan dump their byproduct, steel slag, randomly in open areas, which causes many environmental hazardous problems. This study intended to explore the effectiveness of using fine steel slag aggregate (FSSA) in improving the geotechnical properties of high plastic subgrade soil. First soil and fine steel slag mechanical and engineering properties were evaluating. Then 0%, 5%, 10%, 15%, 20%, and 25% dry weight of soil of fine steel slag (FSSA) were added and mixed into the prepared soil samples. The effectiveness of the FSSA was judged by the improvement in consistency limits, compaction, free swell, unconfined compression strength, and California bearing ratio (CBR). From the test results, it is observed that 20% FSSA additives will reduce plasticity index and free swell by 26.3% and 58.3%, respectively. Furthermore, 20% FSSA additives will increase the unconfined compressive strength, maximum dry density, and CBR value by 100%, 6.9%, and 154%. By conclusion FSSA had a positive effect on the geotechnical properties of the soil and it can be used as admixture in proving geotechnical characteristics of subgrade soil, not only solving the waste disposal problem

Mathematical Modelling for Predicting Thermal Properties of Selected Limestone

Due to a lack of geotechnical and geothermal studies on Jordanian limestone, this paper aims to provide the thermal properties, including thermal conductivity, thermal diffusivity, and specific heat, using the Hot Disk Transient Plane Source (TPS) 2200 method. It also aims to provide a set of mathematical models through which the thermal properties can be indirectly predicted from the rocks’ physical and engineering properties. One hundred cylindrical rock specimens with a height of 20 cm and a diameter of 10 cm were extracted and prepared. The results showed that the thermal conductivity values ranged between (1.931–3.468) (W/(m × k)), thermal diffusivity (1.032–1.81) (mm2/s), and specific heat (1.57–2.563) ((MJ)/(m3 × K)). The results also suggest a direct relationship between conductivity and diffusivity and an inverse relationship between conductivity and specific heat. On the other hand, the results indicate the direct relationship between the conductivity and diffusivity, and the inverse relationship between the specific heat and density, hardness, sound velocity, and rock strength; the opposite happens when the rock’s porosity is considered. Simple regression, multivariate regression, and the backpropagation–artificial neural network (BP–ANN) approach were utilized to predict the thermal properties of limestone. Results indicated that the ANN model provided superior prediction performance compared to other models

Enhancing of uniaxial compressive strength of travertine rock prediction through machine learning and multivariate analysis

Indirect methods for predicting material properties in rock engineering are vital for assessing elastic mechanical properties. Accurately predicting material properties holds significant importance in rock and geotechnical engineering, as it strongly influences decisions about the design and construction of infrastructure projects. Uniaxial compressive strength (UCS) is one of the most important elastic mechanical properties for understanding how rocks and geological formations respond to stress and deformation. However, the standard UCS test faces several challenges, including its destructive nature, high costs, time-consuming procedures, and the requirement for high-quality samples. Therefore, there is a growing demand for indirect methods to estimate UCS, which are invaluable tools for evaluating the elastic mechanical properties of materials. The study aimed to comprehensively analyze the relationships between UCS of travertine rock samples collected from the Dead Sea and Jordan Valley formations and seven different rock indices by utilizing parametric and non-parametric methods. The laboratory results indicate that the study area's travertine rock possesses high-quality and desirable properties. The results reveal that certain rock indices, such as Schmidt hammer, Leeb rebound hardness, and Point Load, strongly correlate with Uniaxial Compressive Strength (UCS). Conversely, other indices, specifically dry density, absorption, pulse velocity, and porosity, exhibit a considerably weaker or very weak relationship with UCS. The paper employs three machine learning techniques, namely the Tree model, k-nearest neighbors (KNN), and Artificial Neural Networks (ANN), to develop predictive models for rock strength. The models were trained on a dataset of rock properties and corresponding mechanical strength values. The study's results revealed that the M5 tree model is the most suitable method for predicting UCS. It demonstrates robust performance across a spectrum of metrics and boasts low prediction errors. Following the M5 tree model are the KNN, ANN, and regression methods in descending order of performance